Robust Kalman Filtering for Signals and Systems with Large Uncertainties by Ian

The Nile on eBay   Robust Kalman Filtering for Signals and Systems with Large Uncertainties by Ian R. Petersen, Andrey V. Savkin

Although the issue of robustness in filtering is not as critical as in feedback control (where there is always the issue of instability to worry about), research on robust filtering and state estimation has remained very active in recent years.

FORMATPaperback LANGUAGEEnglish CONDITIONBrand New Publisher Description

A significant shortcoming of the state space control theory that emerged in the 1960s was its lack of concern for the issue of robustness. However, in the design of feedback control systems, robustness is a critical issue. These facts led to great activity in the research area of robust control theory. One of the major developments of modern control theory was the Kalman Filter and hence the development of a robust version of the Kalman Filter has become an active area of research. Although the issue of robustness in filtering is not as critical as in feedback control (where there is always the issue of instability to worry about), research on robust filtering and state estimation has remained very active in recent years. However, although numerous books have appeared on the topic of Kalman filtering, this book is one of the first to appear on robust Kalman filtering. Most of the material presented in this book derives from a period of research collaboration between the authors from 1992 to 1994. However, its origins go back earlier than that. The first author (LR. P. ) became in­ terested in problems of robust filtering through his research collaboration with Dr. Duncan McFarlane. At this time, Dr. McFarlane was employed at the Melbourne Research Laboratories ofBHP Ltd. , a large Australian min­ erals, resources, and steel processing company.

Table of Contents

1 Introduction.- 2 Continuous-Time Quadratic Guaranteed Cost Filtering.- 3 Discrete-Time Quadratic Guaranteed Cost Filtering.- 4 Continuous-Time Set-Valued State Estimation and Model Validation.- 5 Discrete-Time Set-Valued State Estimation.- 6 Robust State Estimation with Discrete and Continuous Measurements.- 7 Set-Valued State Estimation with Structured Uncertainty.- 8 Robust H? Filtering with Structured Uncertainty.- 9 Robust Fixed Order H? Filtering.- 10 Set-Valued State Estimation for Nonlinear Uncertain Systems.- 11 Robust Filtering Applied to Induction Motor Control.- References.

Review

"The book is primarily a research monograph which presents, in a unified fashion, some recent research on robust Kalman filtering. The book is intended for researchers in robust control and filtering theory, advanced postgraduate students, and engineers with an interest in applying the latest techniques of robust Kalman filtering. Robust Kalman filtering extends the Kalman filtering and the extended Kalman filtering to systems that contain uncertain parameters in addition to the usual white Gaussian noise…. Several examples are given, showing the robust Kalman filters outperforming the regular Kalman filter or the extended Kalman filter. Each of the first ten chapters covers a specific topic, usually with a major theorem characterizing the robust filter followed by an example. The final chapter addresses its application to a particular problem." —Zentralblatt Math

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Long Description

A significant shortcoming of the state space control theory that emerged in the 1960s was its lack of concern for the issue of robustness. However, in the design of feedback control systems, robustness is a critical issue. These facts led to great activity in the research area of robust control theory. One of the major developments of modern control theory was the Kalman Filter and hence the development of a robust version of the Kalman Filter has become an active area of research. Although the issue of robustness in filtering is not as critical as in feedback control (where there is always the issue of instability to worry about), research on robust filtering and state estimation has remained very active in recent years. However, although numerous books have appeared on the topic of Kalman filtering, this book is one of the first to appear on robust Kalman filtering. Most of the material presented in this book derives from a period of research collaboration between the authors from 1992 to 1994. However, its origins go back earlier than that. The first author (LR. P. ) became in

Review Quote

"The book is primarily a research monograph which presents, in a unified fashion, some recent research on robust Kalman filtering. The book is intended for researchers in robust control and filtering theory, advanced postgraduate students, and engineers with an interest in applying the latest techniques of robust Kalman filtering. Robust Kalman filtering extends the Kalman filtering and the extended Kalman filtering to systems that contain uncertain parameters in addition to the usual white Gaussian noise…. Several examples are given, showing the robust Kalman filters outperforming the regular Kalman filter or the extended Kalman filter. Each of the first ten chapters covers a specific topic, usually with a major theorem characterizing the robust filter followed by an example. The final chapter addresses its application to a particular problem." -Zentralblatt Math

Details ISBN1461272092 Author Andrey V. Savkin Short Title ROBUST KALMAN FILTERING FOR SI Series Control Engineering Language English ISBN-10 1461272092 ISBN-13 9781461272090 Media Book Format Paperback Year 2012 Publication Date 2012-11-06 Imprint Springer-Verlag New York Inc. Place of Publication New York Country of Publication United States Affiliation University of Western Australia, Nedlands, WA, Australia Pages 207 DEWEY 621.382 DOI 10.1007/978-1-4612-1594-3 UK Release Date 2012-11-06 AU Release Date 2012-11-06 NZ Release Date 2012-11-06 US Release Date 2012-11-06 Illustrations X, 207 p. Publisher Springer-Verlag New York Inc. Edition Description Softcover reprint of the original 1st ed. 1999 Alternative 9780817640897 Audience Professional & Vocational We've got this

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